Uncertainty Quantification in Machine Learning for Biosignal Applications -- A Review

Autor:	de Jong, Ivo Pascal, Sburlea, Andreea Ioana, Valdenegro-Toro, Matias
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Signal Processing Computer Science - Human-Computer Interaction Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Uncertainty Quantification (UQ) has gained traction in an attempt to fix the black-box nature of Deep Learning. Specifically (medical) biosignals such as electroencephalography (EEG), electrocardiography (ECG), electroocculography (EOG) and electromyography (EMG) could benefit from good UQ, since these suffer from a poor signal to noise ratio, and good human interpretability is pivotal for medical applications and Brain Computer Interfaces. In this paper, we review the state of the art at the intersection of Uncertainty Quantification and Biosignal with Machine Learning. We present various methods, shortcomings, uncertainty measures and theoretical frameworks that currently exist in this application domain. Overall it can be concluded that promising UQ methods are available, but that research is needed on how people and systems may interact with an uncertainty model in a (clinical) environment. Comment: 26 pages, 13 figures, 3 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2312.09454 Zobrazit plný text záznamu View this record from Arxiv